Book Recommendation System 📖

Overview

In this Data Science project, you will see how to build a Book Recommendation System model using Machine Learning Techniques.

Recommendation systems are among the most popular applications of data science. They are used to predict the Rating or Preference that a user would give to an item.

Project structure

The datasets used in this project are:

• BX-Book-Ratings.csv
• BX-Books.csv
• BX-Users.csv

Jupyter Notebook containing the code for data preprocessing and visualization:

• code.ipynb

Steps

1. Data Loading and Exploration:

• Load the datasets using pandas.
• Display the first few rows to understand the structure of the dataset.
• Check for missing values and data types.

2. Visualization:

• Visualize the data on a 2D plot.

3. Data Preprocessing:

• Extract relevant features.
• Group by items and create a new column.

4. k-Nearest Neighbors (kNN)

• Apply the kNN algorithm.
• Convert our table to a 2D matrix, and fill the missing values with zeros.

Results

The system analyzes a reader's preferences based on their reading history and suggests books that are most likely to interest the user.

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in progress...